Essays on exchange rates : a dissertation submitted in fulfilment of the requirements for the degree of Doctor of Philosophy in Finance at Massey University, School of Economics and Finance, Massey University

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Essays on Exchange Rates

A Dissertation Submitted in Fulfilment of the Requirements for the Degree of Doctor of Philosophy in Finance at Massey

University

Vincent Kleinbrod

School of Economics and Finance Massey University

September 2016

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Abstract

This dissertation presents three essays on exchange rates. The reported work builds on the market microstructure approach to exchange rate determination and extends this approach to modelling and forecasting multivariate exchange rate movements, and to a multi-currency trading application.

The first study investigates the role of order flow in explaining joint movements of exchange rate returns, thereby building an original bridge between exchange rate co- movement and the market microstructure literature. We document that absolute order flow differentials have a significant negative effect on future joint currency movements at intraday frequencies. The analysis also shows that other intraday variables, such as the bid–ask spread, have no explanatory power for the co-movements after the absolute order flow differential is accounted for, thereby confirming the robustness of order flow as the driving force for exchange rate correlation. Further analysis demonstrates that absolute order flows also affect conditional variance dynamics.

The second study adds to the findings of the first study. It evaluates the information

content of order flow for accurate predictions of exchange rate co-movement. In line with the

first study, we find that order flow information substantially enhances the accuracy of

covariance forecasts. Moreover, the interest rate differential has a limited role in explaining

and predicting correlation dynamics once the order flow differential is accounted for. The

study concludes by showing the economic value of the order-flow-based covariance

predictions, namely the value of order flow information for covariance predictions beyond

return predictions.

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The third study focuses on the practical relevance of order flow information in foreign

exchange trading. Given the dominance of technical trading among forex professionals, the

study evaluates the value of order flow information for technical traders. Our initial

investigation questions the accuracy of trading signals if these are derived directly from order

flow. We conjecture that the reason for this is that order flow should first be used to generate

exchange rate predictions, which can then be used to derive profitable trading signals. We

examine this conjecture empirically, and the affirmative results highlight the value of order-

flow-based return predictions for technical analysis. Further, we propose a multivariate

trading strategy to boost the benefits of using order flow in technical analysis, which is shown

to be a highly profitable.

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Acknowledgements

I would like to express my sincere gratitude to my supervisors Professor Xiao-Ming Li and Professor David Ding, for their unreserved encouragement and support during my PhD study.

I am especially indebted to my chief supervisor, Professor Xiao-Ming Li, for his constructive guidance and help. Thank you for having confidence in and patience with me. Your enthusiasm and dedication to research will continue to inspire me to become a better researcher.

Special thanks go out to the staff in the School of Economics and Finance who has been very helpful and supportive. The general staff have always made me and other PhD students feel at home. I would like to further acknowledge the support of my fellow PhD students, who have made this journey more colourful and memorable.

Thank you to Massey University for providing financial support for conferences. This thesis benefited from valuable comments from participants of the 2015 Asian FMA Doctoral Consortium, and the 2013–2016 New Zealand finance colloquia. In addition to the conferences, this thesis benefited from valuable comments given by faculty members from the School of Economics and Finance.

Last but not least, I would like to dedicate this thesis to my partner and my parents, in grateful thanks for their patience, support and continued enthusiasm during the project.

Without them, this dissertation would never been feasible.

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List of Tables

Table 2.1 Descriptive statistics ... 30

Table 2.2 Parameter estimates of the GARCH-ADCCXS model ... 35

Table 2.3 Parameter estimates of the GARCH-ADCCXS and GARCH-ADCCXE models ... 39

Table 2.4 Comparisons of intraday frequencies... 45

Table 2.5 Parameter estimates of the GARCH-ADCCXE model with structural change... 53

Table 2.6 Robustness of results: order flow and bid–ask spread ... 57

Table 2.7 Robustness of results: Standardised measures of order flow ... 60

Table 2.8 Parameter estimates of the GARCH-X–ADCCXE model ... 63

Table A.1 Further descriptive statistics... 67

Table A.2 Parameter estimates of the GARCH-ADCCXE model with structural change – further frequencies. 68 Table A.3 Robustness of results: Standardised measures of order flow – further intraday frequencies ... 69

Table A.4 Robustness of results: order flow and bid-ask spread- further intraday frequencies... 70

Table A.5 Order flow and volatility dynamics – further intraday frequencies... 70

Table 3.1 Descriptive statistics ... 90

Table 3.2 Parameter estimates of the GARCH-ADCCXS model ... 92

Table 3.3 Parameter estimates of the GARCH-ADCCXE model (with the IRDs variable added)... 95

Table 3.4 Out-of-sample UMSEs for competing covariance forecasts ... 100

Table 3.5 Out-of-sample UMSEs for ADCCXN, ADCCXP, and ADCCXE ... 105

Table 3.6 Out-of-sample UMSE differences for competing estimation windows ... 108

Table 3.7 Out-of-sample MMSEs for GARCHX-ADCCXS and GARCH-ADCCXS forecasts ... 113

Table 3.8 Out-of-sample economic evaluation of covariance forecasts (EUR–GBP–USD portfolio)... 122

Table 3.9Out-of-sample economic evaluation of covariance forecasts (EUR–JPY–USD portfolio)... 124

Table 3.10 Out-of-sample economic evaluation of competing covariance forecasts (Aim portfolio)... 127

Table B.1 Parameter estimates of the GARCH-ADCCXS and GARCH-ADCCXE models... 131

Table B.2 Robustness of results: order flow and bid–ask spread... 132

Table B.3 Robustness of findings: standardized measures of order flow ... 133

Table 4.1 Intervals for discrete trading recommendations (based on defuzzified output) ... 161

Table 4.2Regression of return predictability from order-flow and lagged returns... 164

Table 4.3Performance of the best trading rule among price- and order-flow-based strategies ... 166

Table 4.4 Out-of-sample accuracy of the Artificial Neural Network (ANN) predictions... 170

Table 4.5. Out-of-sample performance of the regime switching strategy ... 174

Table 4.6Out-of-sample performance of the neuro- and the multi-fuzzy trading strategy... 177

Table 4.7 Out-of-sample performance of the neuro- and the multi-fuzzy trading strategy (triangular membership functions) ... 179

Table 4.8 Out-of-sample performance comparisons of different volatility proxies ... 183

Table 4.9 Out-of-sample performance for simple and fuzzy-logic-based MA rules ... 187

Table C.1Out-of-sample performance of the neuro- and the multi-fuzzy trading strategy (trapezoidal membership functions) ... 190

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List of Figures

Figure 2.1 The ADCCXS news impact surface ... 41

Figure 2.2 The ADCCXE news impact surface... 42

Figure 2.3 Factual and counterfactual representation of the ADCCXE estimates... 47

Figure 2.4 Unrestricted vs. restricted ADCCXE ... 48

Figure 2.5 Conditional correlation dynamics for EUR–GBP, EUR–JPY and GBP–JPY... 50

Figure 2.6 GARCH-X and GARCH variance dynamics ... 64

Figure 3.1 Graphical UMSEs of the ADCCXS, ADCC and DCC covariance predictions... 102

Figure 3.2 Graphical UMSEs of different window sizes ... 110

Figure 3.3 Graphical MMSEs of GARCH-X and GARCH-based predictions... 114

Figure 4.1 Graphical representation of the multivariate fuzzy trading strategy... 153

Figure 4.2 A three-layer feed-forward neural network ... 155

Figure 4.3 Graphical representation of the membership functions and fuzzy sets... 158

Figure 4.4 Comparison of multivariate and univariate fuzzy logic framework ... 162

Figure C.1 Triangular and trapezoidal membership functions... 191